Job distribution within a grid environment

ABSTRACT

According to one aspect of the present disclosure, a technique for job distribution within a grid environment includes receiving a job at a submission cluster for distribution of the job to at least one of a plurality of execution clusters where each execution cluster includes one or more execution hosts. Resource attributes are determined corresponding to each execution host of the execution clusters. For each execution cluster, execution hosts are grouped based on the resource attributes of the respective execution hosts. For each grouping of execution hosts, a mega-host is defined for the respective execution cluster where the mega-host for a respective execution cluster defines resource attributes based on the resource attributes of the respective grouped execution hosts. An optimum execution cluster is selected for receiving the job based on a weighting factor applied to select resources of the respective execution clusters.

BACKGROUND

The increasing complexity of electronic tasks, often referred to as“jobs” (e.g. executable programs such as computational tasks, commandexecution, data collection, etc.) has increased the demand for resourcesused in accomplishing such tasks. Resources may include hardware thataids in completing electronic tasks, such as servers, clients, mainframecomputers, networks, network storage, databases, memory, CPU time, etc.Resources may also include software, available network services,software licenses, and other non-hardware resources. One response to theincreased demand for resources has been the development of networkedcomputing grid systems, which operate to integrate resources fromotherwise independent grid participants. Computing grid systemsgenerally include hardware and software infrastructure configured toform a virtual organization comprised of multiple resources in oftengeographically disperse locations. Electronic tasks typically requirecertain amounts and/or types of resources for completion. Once a job iscreated, it needs to be assigned, or scheduled, to sufficient andcompatible resources within a computing grid system for processing. Forexample, some resources may be ranked for determining which resource(s)should be used for processing submitted jobs, such as forecastingresource utilization based on historical statistics, runtime clusterloads, etc. Jobs may also be assigned to certain resources based onavailability of data or applications needed to process the job.

BRIEF SUMMARY

According to one aspect of the present disclosure a method, system,computer program product, and technique for job distribution within agrid environment is disclosed. In one aspect, a method includesreceiving jobs at a submission cluster for distribution of the jobs toat least one of a plurality of execution clusters where each executioncluster includes one or more execution hosts. Resource attributes aredetermined corresponding to each execution host of the executionclusters. For each execution cluster, execution hosts are grouped basedon the resource attributes of the respective execution hosts. For eachgrouping of execution hosts, a mega-host is defined for the respectiveexecution cluster where the mega-host for a respective execution clusterdefines resource attributes based on the resource attributes of therespective grouped execution hosts. Resource requirements for the jobsare determined, and candidate mega-hosts are identified for the jobsbased on the resource attributes of the respective mega-hosts and theresource requirements of the jobs. An optimum execution cluster isselected for receiving the job based on a weighting factor applied toselect resources of the respective execution clusters.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

For a more complete understanding of the present application, theobjects and advantages thereof, reference is now made to the followingdescriptions taken in conjunction with the accompanying drawings, inwhich:

FIG. 1 is an embodiment of a network of data processing systems in whichthe illustrative embodiments of the present disclosure may beimplemented;

FIG. 2 is an embodiment of a data processing system in which theillustrative embodiments of the present disclosure may be implemented;

FIG. 3 is a diagram illustrating an embodiment of a data processingsystem for job distribution within a grid environment in whichillustrative embodiments of the present disclosure may be implemented;

FIG. 4 is a diagram illustrating is a diagram illustrating a method andtechnique for job grouping and scheduling in a grid environmentaccording to the present disclosure;

FIG. 5 is a diagram illustrating a hierarchy fair-share tree example ofgrid resource share allocation according to the present disclosure; pFIG. 6 is a diagram illustrating a job transition process using afair-share-forwarding policy in a grid environment according to thepresent disclosure;

FIG. 7 is a flow diagram illustrating an embodiment of a method for jobdistribution within a grid environment according to the presentdisclosure; and

FIG. 8 is a flow diagram illustrating another embodiment of a method forjob distribution within a grid environment according to the presentdisclosure.

DETAILED DESCRIPTION

Embodiments of the present disclosure provide a method, system andcomputer program product for job distribution within a grid environment.For example, in some embodiments, the method and technique includes:receiving jobs at a submission cluster for distribution of the jobs toat least one of a plurality of execution clusters, each executioncluster comprising one or more execution hosts; determining resourceattributes corresponding to each execution host of the executionclusters; grouping, for each execution cluster, execution hosts based onthe resource attributes of the respective execution hosts; defining, foreach grouping of execution hosts, a mega-host for the respectiveexecution cluster, the mega-host for a respective execution clusterdefining resource attributes based on the resource attributes of therespective grouped execution hosts; determining resource requirementsfor the jobs; and identifying candidate mega-hosts for the jobs based onthe resource attributes of the respective mega-hosts and the resourcerequirements of the jobs. Thus, in some embodiments of the presentdisclosure, the resources of back-end execution hosts are grouped basedon the same or similar resources to define mega-host resourcedefinitions. A course granularity matching process is performed betweenthe resource requirements of the submitted jobs and the resourcedefinitions of the mega-hosts to quickly identify candidate mega-hosts(and thus corresponding execution clusters) for job processing. Further,embodiments of the present disclosure group the submitted jobs accordingto the resource requirements of the jobs, and the users or user groupssubmitting the jobs, to efficiently schedule groups of jobs to theback-end execution clusters. Additionally, various resource utilizationand job forwarding techniques are utilized, such as fair-share policiesregarding resource utilization (including a dynamic priority forusers/user groups), a dynamic pending job queue length for executionclusters, a forwarding resource ratio for various resources of theexecution clusters (which may be applied on a resource-specific basis,applied to all resources of a particular type, applied to particularexecution clusters, applied to all execution clusters, etc.) and/or acluster selection process that considers host-based resources and sharedresources, including a weighting factor that may be applied to thevarious resources, to efficiently select the optimum execution clusterfor job processing.

As will be appreciated by one skilled in the art, aspects of the presentdisclosure may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present disclosure may take theform of an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer usable or computer readablemedium(s) may be utilized. The computer readable medium may be acomputer readable signal medium or a computer readable storage medium. Acomputer readable storage medium may be, for example but not limited to,an electronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer readable storage medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer readable storage medium may be any tangible medium that cancontain, or store a program for use by or in connection with aninstruction execution system, apparatus or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present disclosure are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in acomputer-readable medium that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablemedium produce an article of manufacture including instruction meanswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide processes for implementing the functions/actsspecified in the flowchart and/or block diagram block or blocks.

With reference now to the Figures and in particular with reference toFIGS. 1-2, exemplary diagrams of data processing environments areprovided in which illustrative embodiments of the present disclosure maybe implemented. It should be appreciated that FIGS. 1-2 are onlyexemplary and are not intended to assert or imply any limitation withregard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environments may bemade.

FIG. 1 is a pictorial representation of a network of data processingsystems in which illustrative embodiments of the present disclosure maybe implemented. Network data processing system 100 is a network ofcomputers in which the illustrative embodiments of the presentdisclosure may be implemented. Network data processing system 100contains network 130, which is the medium used to provide communicationslinks between various devices and computers connected together withinnetwork data processing system 100. Network 130 may include connections,such as wire, wireless communication links, or fiber optic cables.

In some embodiments, server 140 and server 150 connect to network 130along with data store 160. Server 140 and server 150 may be, forexample, IBM® Power Systems™ servers. In addition, clients 110 and 120connect to network 130. Clients 110 and 120 may be, for example,personal computers or network computers. In the depicted example, server140 provides data and/or services such as, but not limited to, datafiles, operating system images, and applications to clients 110 and 120.Network data processing system 100 may include additional servers,clients, and other devices.

In the depicted example, network data processing system 100 is theInternet with network 130 representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. At the heart of the Internet is a backbone ofhigh-speed data communication lines between major nodes or hostcomputers, consisting of thousands of commercial, governmental,educational and other computer systems that route data and messages. Ofcourse, network data processing system 100 also may be implemented as anumber of different types of networks, such as for example, an intranet,a local area network (LAN), or a wide area network (WAN). FIG. 1 isintended as an example, and not as an architectural limitation for thedifferent illustrative embodiments.

FIG. 2 is an embodiment of a data processing system 200 such as, but notlimited to, client 110 and/or server 140 in which an embodiment of asystem for job distribution within a grid environment according to thepresent disclosure may be implemented. In this embodiment, dataprocessing system 200 includes a bus or communications fabric 202, whichprovides communications between processor unit 204, memory 206,persistent storage 208, communications unit 210, input/output (I/O) unit212, and display 214.

Processor unit 204 serves to execute instructions for software that maybe loaded into memory 206. Processor unit 204 may be a set of one ormore processors or may be a multi-processor core, depending on theparticular implementation. Further, processor unit 204 may beimplemented using one or more heterogeneous processor systems in which amain processor is present with secondary processors on a single chip. Asanother illustrative example, processor unit 204 may be a symmetricmulti-processor system containing multiple processors of the same type.

In some embodiments, memory 206 may be a random access memory or anyother suitable volatile or non-volatile storage device. Persistentstorage 208 may take various forms depending on the particularimplementation. For example, persistent storage 208 may contain one ormore components or devices. Persistent storage 208 may be a hard drive,a flash memory, a rewritable optical disk, a rewritable magnetic tape,or some combination of the above. The media used by persistent storage208 also may be removable such as, but not limited to, a removable harddrive.

Communications unit 210 provides for communications with other dataprocessing systems or devices. In these examples, communications unit210 is a network interface card. Modems, cable modem and Ethernet cardsare just a few of the currently available types of network interfaceadapters. Communications unit 210 may provide communications through theuse of either or both physical and wireless communications links.

Input/output unit 212 enables input and output of data with otherdevices that may be connected to data processing system 200. In someembodiments, input/output unit 212 may provide a connection for userinput through a keyboard and mouse. Further, input/output unit 212 maysend output to a printer. Display 214 provides a mechanism to displayinformation to a user.

Instructions for the operating system and applications or programs arelocated on persistent storage 208. These instructions may be loaded intomemory 206 for execution by processor unit 204. The processes of thedifferent embodiments may be performed by processor unit 204 usingcomputer implemented instructions, which may be located in a memory,such as memory 206. These instructions are referred to as program code,computer usable program code, or computer readable program code that maybe read and executed by a processor in processor unit 204. The programcode in the different embodiments may be embodied on different physicalor tangible computer readable media, such as memory 206 or persistentstorage 208.

Program code 216 is located in a functional form on computer readablemedia 218 that is selectively removable and may be loaded onto ortransferred to data processing system 200 for execution by processorunit 204. Program code 216 and computer readable media 218 form computerprogram product 220 in these examples. In one example, computer readablemedia 218 may be in a tangible form, such as, for example, an optical ormagnetic disc that is inserted or placed into a drive or other devicethat is part of persistent storage 208 for transfer onto a storagedevice, such as a hard drive that is part of persistent storage 208. Ina tangible form, computer readable media 218 also may take the form of apersistent storage, such as a hard drive, a thumb drive, or a flashmemory that is connected to data processing system 200. The tangibleform of computer readable media 218 is also referred to as computerrecordable storage media. In some instances, computer readable media 218may not be removable.

Alternatively, program code 216 may be transferred to data processingsystem 200 from computer readable media 218 through a communicationslink to communications unit 210 and/or through a connection toinput/output unit 212. The communications link and/or the connection maybe physical or wireless in the illustrative examples.

The different components illustrated for data processing system 200 arenot meant to provide architectural limitations to the manner in whichdifferent embodiments may be implemented. The different illustrativeembodiments may be implemented in a data processing system includingcomponents in addition to or in place of those illustrated for dataprocessing system 200. Other components shown in FIG. 2 can be variedfrom the illustrative examples shown. For example, a storage device indata processing system 200 is any hardware apparatus that may storedata. Memory 206, persistent storage 208, and computer readable media218 are examples of storage devices in a tangible form.

FIG. 3 is an illustrative embodiment of a system 300 for jobdistribution within a grid environment. Various components of system 300may be implemented on data processing systems or platforms such as, butnot limited to, servers 140 and/or 150, clients 110 and/or 120, or atother data processing system locations. In FIG. 3, system 300illustrates an exemplary grid architecture 302 where a plurality ofclusters are configured to communicate with one another and/or shareresources across the grid. Each cluster may include a plurality oflinked machines or “hosts” which are configured to provide resourcessuch as CPU time, database storage, software licenses, and computingcapabilities. A host may be any machine capable of providing resources,such as a personal computer (PC), a server, or other type of computingdevice. Resources on a particular host may be divided into “slots” whichgenerally refer to certain amounts of electronic task or job capacity onthe host.

In the embodiment illustrated in FIG. 3, system 300 includes asubmission cluster 310 and one or more execution clusters 312 (e.g.,execution clusters 312 ₁-312 _(n)). Submission cluster 310 may comprisea single host or multiple hosts and is configured to receive jobs 314(e.g., job₁-job_(n)) from one or more clients or users 316 (e.g.,user₁-user_(n)). In some embodiments, submission cluster 310 isconfigured to support a large scale cluster or grid environment whileproviding single system images to users 316. Submission cluster 310receives jobs 314 from users 316, analyzes the resource requirements ofthe submitted jobs 314, and performs various scheduling decisions toschedule and/or forward jobs 314 to back-end execution clusters 312 forexecution/processing. For example, in the embodiment illustrated in FIG.3, submission cluster 310 includes a meta-scheduler 320 and job resourcerequirement data 322. Resource requirement data 322 may compriseinformation regarding the resource requirements for particular jobs 314.For example, each job 314 may require certain resources (e.g., a certainnumber of servers, particular operating system, certain licensedsoftware applications, memory capacity, particular processor or CPUcapabilities, require a certain number of job slots, project name,etc.). Meta-scheduler 320 performs various resource matching evaluationsbased on the resource requirements of the different jobs 314 and thecluster resource attributes and availability to process the jobs 314(e.g., the resource attributes and availability of execution clusters312). It should be understood that in some embodiments, submissioncluster 310 may hold a certain number of pending jobs 314 and mayinclude a local scheduler for processing a certain number of jobs 314using local submission cluster 310 resources while other jobs areforwarded to execution clusters 312 for processing. Meta-scheduler 320may be implemented in any suitable manner using known techniques thatmay be hardware-based, software-based, or some combination of both. Forexample, meta-scheduler 320 may comprise software, logic and/orexecutable code for performing various functions as described herein(e.g., residing as software and/or an algorithm running on a processorunit, hardware logic residing in a processor or other type of logicchip, centralized in a single integrated circuit or distributed amongdifferent chips in a data processing system).

In the embodiment illustrated in FIG. 3, each execution cluster 312includes a cluster master 330 and 332 having a respective scheduler 334and 336 and resource manager 338 and 340. For ease and claritydescription, various components and functions described in connectionwith execution clusters 312 ₁ and 312 ₂ may not be described and/orillustrated with respect to execution cluster 312 _(n); however, itshould be understood that additional execution clusters 312 may includesimilar components and/or functionality. Each cluster master 330 and 332may comprise a master host associated with the respective executioncluster 312 ₁ and 312 ₂ configured with various administration and/ormanagement functions corresponding to the respective execution cluster312 ₁ and 312 ₂. For example, each scheduler 334 and 336 may performlocal scheduling functions for jobs 314 received by the respectiveexecution cluster 312 ₁ and 312 ₂ from submission cluster 310. Resourcemanagers 338 and 340 may gather and/or otherwise collect resourceattribute information corresponding to each host of the respectiveexecution cluster 312 ₁ and 312₂. Schedulers 334 and 336 and/or resourcemanagers 338 and 340 may be implemented in any suitable manner usingknown techniques that may be hardware-based, software-based, or somecombination of both. For example, schedulers 334 and 336 and/or resourcemanagers 338 and 340 may comprise software, logic and/or executable codefor performing various functions as described herein (e.g., residing assoftware and/or an algorithm running on a processor unit, hardware logicresiding in a processor or other type of logic chip, centralized in asingle integrated circuit or distributed among different chips in a dataprocessing system).

As described above, each execution cluster 312 includes one or moreexecution hosts 344 each with various resource attributes (e.g., hosttype, host model, slots, maximum memory capacity, maximum swap, NCPU,host level Boolean resources, etc.). Resource managers 338 and 340 maycollect and/or otherwise gather resource attribute informationassociated with the various execution hosts 344 of the respectiveexecution cluster 312 ₁ and 312 ₂ and provide the resource attributeinformation to submission cluster 310 (e.g., to meta-scheduler 320). Forexample, in the illustrated embodiment, execution cluster 312 ₁ includesexecution hosts 350 ₁-350 _(n), and execution cluster 312 ₂ includesexecution hosts 352 ₁-352 _(n). Some of execution hosts 350 ₁-350 _(n)may include the same or similar resource attributes (or resourceattributes falling within some defined range or resource category), andsome of execution hosts 352 ₁-352 _(n) may include the same or similarresource attributes (or resource attributes falling within some definedrange or resource category). Meta-scheduler 320 groups, sorts and/orotherwise combines execution hosts 344 having the same or similarresource attributes or resource attributes meeting some defined criteriafor each respective execution cluster 312 and defines and/or models oneor more mega-hosts 360 for each execution cluster 312. For example, inthe embodiment illustrated in FIG. 3, submission cluster 310 includesmega-host resource attribute data 370 comprising informationcorresponding to each mega-host 360 ₁-360 _(n) defined for a respectiveexecution cluster 312. In FIG. 3, a mega-host 360 ₁ is defined forexecution cluster 312 ₁, a mega-host 360 ₂ is also defined for executioncluster 312 ₁, a mega-host 360 ₃ is defined for execution cluster 312 ₂,etc. Each mega-host 360 ₁ and 360 ₂ (and any other mega-hosts 360defined for execution cluster 312 ₁) may define a different set ofresource attributes corresponding to a particular set or grouping ofexecution hosts 344 of execution cluster 312 ₁ (e.g., one or more ofexecution hosts 350 ₁-350 _(n)). Similarly, mega-host 360 ₃ (and anyother mega-hosts 360 defined for execution cluster 312 ₂) defines adifferent set of resource attributes corresponding to a particular setor grouping of execution hosts 344 of execution cluster 312 ₂. Thedefined criteria for determining mega-host 360 groupings may vary (e.g.,based on slots, memory capacity attributes, host-based resourceattributes, shared resources, or other types of resource attributes)such that execution hosts 344 meeting the mega-host criteria or havingresource attributes defined in the mega-host criteria are included inthe respective mega-host 360.

As a further example, consider the following simplified resourceattributes corresponding to various execution hosts 344 of executioncluster 312 ₁:

EH350 ₁: 4 slots, 16G memory capacity

EH350 ₂: 4 slots, 16G memory capacity

EH350 ₃: 4 slots, 16G memory capacity

EH350 ₄: 4 slots, 32G memory capacity

EH350 ₅: 4 slots, 32G memory capacity

where EH350 ₁-EH350 ₅ represent five different execution hosts 344within execution cluster 312 ₁, execution hosts EH350 ₁-EH350 ₃ eachcontain four slots with a sixteen gigabyte maximum memory capacity, andexecution hosts EH350 ₄-EH350 ₅ each contain four slots with athirty-two gigabyte maximum memory capacity. Further, consider thefollowing simplified resource attributes corresponding to variousexecution hosts 344 of execution cluster 312 ₂:

EH352 ₁: 8 slots, 64G memory capacity

EH352 ₂: 8 slots, 64G memory capacity

EH352 ₃: 8 slots, 64G memory capacity

EH352 ₄: 8 slots, 64G memory capacity

EH352 ₅: 8 slots, 64G memory capacity

where EH352 ₁-EH352 ₅ represent five different execution hosts 344within execution cluster 312 ₂, and each of execution hosts EH352₁-EH352 ₅ contains eight slots with a sixty-four gigabyte maximum memorycapacity. Meta-scheduler 320 groups, for each execution cluster 312,execution hosts 344 with the same or similar key resource attributes.Thus, in this example, meta-scheduler 320 may define the followingmega-hosts 360:

MH360 ₁ (MH₁/Cluster₁): 12 slots, 16G memory capacity

MH360 ₂ (MH₂/Cluster₁): 8 slots, 32G memory capacity

MH360 ₃ (MH₃/Cluster₂): 40 slots, 64G memory capacity

where MH360 ₁ and MH360 ₂ represent mega-hosts 360 for execution cluster312 ₁ defining a set of resource attributes corresponding to executionhosts 344 of execution cluster 312 ₁ having the same or similar resourceattributes. MH360 ₃ represents a mega-host 360 for execution cluster 312₂ defining a set of resource attributes corresponding to execution hosts344 of execution cluster 312 ₂ having the same or similar resourceattributes.

Accordingly, in operation, meta-scheduler 320 analyzes job resourcerequirement data 322 corresponding to received jobs 314 and performs aresource matching evaluation to mega-host 360 resource attributes toidentify candidate mega-hosts 360 (and thus corresponding executionclusters 312) for processing the received jobs 314. Thus, by performinga coarse granularity resource matching of job resource requirements tomega-host 360 resource attribute information, meta-scheduler 320determines which execution clusters 312 have resources that will satisfythe job resource requirements without having to evaluate each executionhost of each execution cluster. Using the above example, if a job 314 ₁has a resource requirement of a sixty-four gigabyte memory capacity,meta-scheduler 320 may compare the resource requirements against theresource attributes of each mega-host 360 and identify candidatemega-hosts 360 meeting the resource requirements of job 314 ₁ (e.g.,mega-host 360 ₃ as a candidate mega-host 360 for job 314 ₁ processing),and thereby identify candidate execution clusters 312 having theresources to satisfy the resource requirements of job 314 ₁ (e.g.,execution cluster 312 ₂). Meta-scheduler 320 may also dynamically andautomatically perform execution host 344 grouping, sorting and/orotherwise combining for defining and/or modeling one or more mega-hosts360 for each execution cluster 312 based on changes in resourceattributes of particular execution hosts 344 (e.g., if a new Booleanresource is set to a particular execution host 344 in a particularexecution cluster 312, and the Boolean resource is part of the desiredor predefined mega-host grouping criteria, meta-scheduler 320 mayautomatically remove the execution host 344 from an existing mega-host360 that may not contain the Boolean resource and add or join theexecution host 344 to another mega-host 360 that contains the Booleanresource).

FIG. 4 is a diagram illustrating a method and technique for job groupingand scheduling in a grid environment according to the presentdisclosure. In the illustrated embodiment, meta-scheduler 320 groups,organizes and/or otherwise sorts jobs 314 based on various policies aswell as the resource requirements of the respective jobs. For example,in the illustrated embodiment, meta-scheduler 320 organizes jobs 320based on priority policies into one or more priority queues 410. In FIG.4, two priority queues 410 ₁ and 410 ₂ are illustrated; however, itshould be understood that the quantity of queues 410 may vary based on aquantity of different queue levels or priorities (e.g., each level orqueue associated with a different priority of job processing). In theillustrated embodiment, queue 410 ₁ may be considered a high priorityqueue 410 while queue 410 ₂ may be considered a low priority queue 410.Each job 314 may define a priority level as well as the resourceattributes needed to satisfy the respective job 314. Meta-scheduler 320organizes and/or sorts the jobs 314 into resource requirements “buckets”or groups based on the same or similar job resource requirements. InFIG. 4, three different levels and/or categories of job resourcerequirement groups 422 are illustrated (e.g., identified as RESREQ₁ 422₁, RESREQ₂ 422 ₂ and RESEQ₃ 422 ₃). In FIG. 4, jobs 314 are furtherorganized, sorted and/or grouped based on a particular user 316submitting the job 314. In the illustrated embodiment, user₁ 316 ₁ hasthree submitted jobs 314 with RESREQ₁ 422 ₁ resource requirements, user₂316 ₂ has two submitted jobs 314 with RESREQ₁ 422 ₁ resourcerequirements, user₃ 316 ₃ has one submitted job 314 with RESREQ₂ 422 ₂resource requirements, and user₄ 316 ₄ has one submitted job 314 withRESREQ₂ 422 ₂ resource requirements. Additionally, user₂ 316 ₂ has threesubmitted jobs 314 with RESREQ₃ 422 ₃ resource requirements, user₃ 316 ₃has four submitted jobs 314 with RESREQ₃ 422 ₃ resource requirements,and a user₅ 316 ₅ has four submitted jobs 314 with RESREQ₃ 422 ₃resource requirements. As illustrated in FIG. 4, jobs 314 are groupedbased on the resource requirements of the respective jobs 314 and theuser 316 submitting the job 314. The jobs 314 are also sorted and/orotherwise grouped based on priority policies into queues 410 ₁ and 410₂.

In operation, meta-scheduler 320 will analyze, determine and/orotherwise match resource requirement buckets or groups 422 to mega-hostresource attribute data 370 to identify candidate mega-hosts 360 foreach resource requirement group 422 of jobs 314. At the end of thematching process, mega-hosts 360 that cannot satisfy the resourcerequirements of the resource requirement group 422 will have beenremoved and/or filtered out of a list of candidate mega-hosts 360 thatcan satisfy the resource requirements of the group 422. In someinstances, because many jobs 314 submitted have the same or similarresource requirements and may be submitted by the same user, organizingor sorting the jobs 314 by resource requirements and/or submitting userprovides greater efficiency in identifying candidate execution clusters312 for processing the jobs 314. During allocation of jobs 314 toparticular execution clusters 312, meta-scheduler 320 may select jobs314 based on priority policies (e.g., selecting from priority queues 410₁ or 410 ₂) and schedule jobs 314 against candidate mega-hosts 360.Thus, for example, if during a particular scheduling cycle a particularjob 314 corresponding to a particular resource requirement group 422 hasbeen evaluated and marked “cannot be forwarded” or is otherwisedetermined that resources are currently unavailable to process thecorresponding job 314, all other jobs belonging to the same resourcerequirement group 422 may be ignored during this scheduling cycle.

As described above, meta-scheduler 320 may forward jobs 314 to back-endexecution clusters 312 based on a number of different job forwardingpolicies, alone or in combination. In some embodiments, system 300 useslogical resources to represent physical resources in grid environment302. For example, resources may be categorized by several ways, such asby scope: host-based resources and shared resources. Host-basedresources are associated with individual hosts and represent theattributes of the hosts. Examples of host-based resources may includehost type, host model, maximum memory, maximum swap, total number ofslots/cpus and total available memory etc. Shared resources are notassociated with individual hosts but are instead shared by the entirecluster or a subset of hosts within the cluster. Examples of sharedresources are software licenses or disk space on shared file systemsthat is mounted by several hosts. Resources may also be categorized byvalues: Boolean resources, numeric resources and string resources. Forinstance, “slot” can be a numeric resource to represent number of jobslots on a host, “bigmem” can be a boolean resource to represent thathost has a large RAM, “Fluent” can be a numeric resource that representsa total number of available fluent licenses in the cluster,“FluentVersion” can be a string resource that represents the versionstring of FLUENT software in the cluster. The Boolean resource is ahost-based resource, while numeric and string resources can be eitherhost-based or shared resources. Only a numeric resource can beconsumable that can be allocated to a workload. Resources can also becategorized by the way values change: dynamic resources and staticresources. Dynamic resources are the resources that change their valuesdynamically, for instance, available memory. Static resources are theresources that do not change their values.

In some embodiments of the present disclosure, resources are defined ineach execution cluster 312 and will be used for back-end executioncluster 312 local job scheduling. In order for meta-scheduler 320 to beresource aware, the resource information will be collected and updatedto meta-scheduler 320 periodically (requested and/or obtained bymeta-scheduler 320 or pushed by respective execution clusters 312 tometa-scheduler 320). For resources defined in mega-host 360 matchingcriteria, the resources become attributes of the mega-host 360. Amongthem, numeric and string resources will preserve the value, forinstance, “host type of mega-host is Linux”. Some of the numeric sharedresources will be kept independent from mega-host based resources, likefloating license resources for meta-scheduler 320 consideration.Remaining resources may become a Boolean attribute of a mega-host 360,whether the resource exists or not.

As described above, meta-scheduler 320 schedules and/or forwards jobs314 to execution clusters 312 for processing. Each execution cluster 312maintains some number of pending jobs 314 in a pending job queue.Instead of defining an arbitrary number for a pending job queue length,meta-scheduler 320 uses a dynamic method to calculate this number basedon resource capacity and job resource requests/requirements. Aforwarding resource ratio is used by meta-scheduler 320 to derive thenumber of jobs 314 to allocate to a pending job queue of a respectiveexecution cluster 312. The ratio is used to calculate total forwardresources (Total_forward_(res)) based on total resource capacity(Total_(res)) as set forth below:

Total_forward_(res)−Forward_Ratio*Total_(res)

This is a generic formula applying to different types of resources“res”. Meta-scheduler 320 uses total forward resources(Total_forward_(res)) to schedule and control the number of forwardedjobs 314 according to job requests. This determines the number pendingjobs for an execution cluster 312 as set forth below:

Σ_(i=1) ^(N)Alloc_(res)(R _(i))+Σ_(j=1) ^(M)Ask_(res)(FP_(j))<=Total_foward_(res)

Σ_(i=1) ^(N)Alloc_(res)(R _(i))<−Total_forward_(res)−Σ_(i=1)^(N)Ask_(res)(R _(i))

where Alloc_(res)(R) is the amount of resources allocated to running jobR, Ask_(res)(FP) is the amount of resource requested by a forwardedpending job FP, N is the number of running jobs using the resource“res”, and M is the number of forwarded pending jobs requesting theresource “res”. Since the number of jobs 314 requesting a resource maybe different, this resource capacity aware approach dynamically adjustspending queue length for an execution cluster 312 based on the job 314request and the best execution cluster 312 to satisfy the needs of thejob 314. The forwarding resource ratio can be defined in multiple levels(e.g., system wide (e.g., applying to all types of resources in allexecution clusters 312), execution cluster wide (e.g., applying to allresources in a specific execution cluster 312), resource type (e.g.,applying to a defined resource type for all execution clusters 312 orfor specified execution clusters 312) or mega-host level (e.g., applyingto defined mega-host 360 slots in a specified execution cluster 312).Each one controls a different scope, and a lower level may have anarrower scope. For example, if total number of slots for one executioncluster is 100 and the ratio is 1.5, total forward slots for thisexecution cluster 312 will be 150. If all jobs 314 are asking for oneslot, meta-scheduler 320 can forward 150 jobs 314 to the back-endexecution cluster 312. If the number of running jobs is 100, there willbe 50 pending jobs. On the other hand, if all jobs are asking for twoslots, meta-scheduler 320 can forward 75 jobs to the back-end executioncluster 312. In this case, if the number of running jobs is 50, therewill be 25 pending jobs. A lower level ratio defines more specific andnarrowing scope and will overwrite higher level ratio definitions. Forinstance, a resource type base ratio (applying to all resources of aparticular type for all execution clusters 312) may be configured towill overwrite both execution cluster wide and system wide ratios. Hereare a few examples. If the system wide ratio is 1.5, if executioncluster A has 100 slots and 50 licA licenses in total, the total forwardslots will be 150 and total forward licA licenses will be 75. Ifexecution cluster B has 200 slots and 20 licB licenses, the totalforward slots will be 300 and total forward licB licenses will be 30. Ifthe system wide ratio is 1.5, but execution cluster B defines adifferent ratio for licB (2.0), in this case, total forward slots willremain as the same as previous case, 150 for execution cluster A and 300for execution cluster B. Total forward licA licenses will be 75, whiletotal forward licB licenses will be 40. Multiple level forwardingresource ratios enable flexibility to serve different conditions. Forinstance, if one execution cluster 312 is designed to handle lots ofshort jobs 314, this execution cluster 312 forwarding ratio can be set alarger value to allow a larger job queue length in order to maintainhigh throughput and utilization.

As indicated above, jobs 314 submitted to submission cluster 310 have aresource requirement. In some embodiments, this job resource requirementas a whole will be forwarded to the respective execution cluster 312 andeventually evaluated by a respective back-end scheduler 334/336 whendoing local job scheduling. A typical resource requirement evaluation ina back-end execution cluster 312 may include two parts. The first partis to evaluate an expression statement of the resource requirementagainst each execution cluster host 344 to check the existence of theneeded resource and its value (e.g., to identify candidate executionhosts 344 for the job). For example, the expression/statement“select[defined(licA) && mem>1000]” may indicate a request to identifyan execution host 344 that can access a licA resource and has availablememory greater than 1000 megabytes. The second part of the resourcerequirement evaluation is to check resource availability and perform areservation for processing the job. For instance, if a job requestsneeds two slots and one licA resource, the local scheduler 334/336 willtry to identify execution hosts 344 to run the job and reserve two slotsand one licA resource.

In some embodiments, meta-scheduler 320 evaluates job resourcerequirement data 322 corresponding to submitted jobs 314. For example,if a particular job 314 requests a “bigmem” execution host 344,meta-scheduler 320 should not forward this job 314 to an executioncluster 312 that does not have this type of resource. However, due tothe complexity of resource evaluation and dynamic changing nature ofcertain resources, it may be inefficient and/or unnecessary formeta-scheduler 320 to evaluate the full job resource requirement for aparticular job 314. For example, the available memory of a particularexecution host 344 may change dynamically. If one execution host 344temporarily cannot satisfy a job memory requirement, this may only meanthat this particular job cannot use this particular execution host 344at the present time (but may be able to use it later). In this case, thejob can still be forwarded to the particular execution host 344. In someembodiments, meta-scheduler 320 only evaluates a subset of the jobresource requirement. For scheduling efficiency purposes, meta-scheduler320 will evaluate the resource requirement against mega-hosts 360instead of individual execution hosts 344. This simplified evaluationmay be defined by the following: 1) checking existence of all resourcesin the resource requirement; 2) checking the value of resources definedin mega-host 360 matching criteria; 3) checking the maximal value ofselected dynamic resources; and 4) checking availability of selectedreserved resources. In the first phase, all resources appearing in theresource requirement for a job will be checked for their existence foreach mega-host 360. For example, for the requirement “select[bigmem]rusage[licA=1]”, meta-scheduler 320 will check both bigmem Booleanresources and licA numeric shared resources. If any resource does notexist or cannot be used by mega-host 360, the particular mega-host 360will be ignored. If any resource does not exist in a particularexecution cluster 312 as defined by the mega-host 360 definition, theexecution cluster 312 will be ignored for job forwarding scheduling. Inthe second phase, for all resources appearing in an expressionstatement, if a resource is a numeric or string resource defined inmega-host 360 matching criteria, meta-scheduler 320 will perform theevaluation and check the value. For example, for theexpression/statement “select[fluentVersion==“version10”]”,meta-scheduler 320 will check if the fluentVersion string resource valueon a particular mega-host 360 is equals to “version10”. If the value ofresource does not satisfy the resource requirement, the evaluation willfail and the particular mega-host 360 (or execution cluster 312) will beignored for job forwarding scheduling. In phase three, since the valueof dynamic resources may change, if checking the resource requirementrequires value checking for a dynamic resource, meta-scheduler 320 willignore this checking requirement. However, if a maximal value of adynamic resource is known, meta-scheduler 320 will use those values toconduct the checking. For example, for total available memory and totalavailable swap, the corresponding maximal values are known. If theresource requirement requests “select[mem>1000]”, meta-scheduler 320will use the maximal values of mega-hosts 360 to replace availablememory and perform the corresponding checking. If the checking fails tomeet the requirement, it means the mega-host 360 (and execution cluster312) can never satisfy the requirement, and thus the mega-host 360 (andexecution cluster 312) will be ignored for job forwarding scheduling. Inphase four, for a resource reservation requirement, meta-scheduler 320checks total availability of the resource. If the amount of availabletotal forward resource cannot satisfy the job requirement, the mega-host360 (and execution cluster 312) will be ignored for job forwardingscheduling. For all reservation resources, the following conditionchecking should be satisfied in order to make a particular mega-host 360(or execution cluster 312) a candidate:

Σ_(i=1) ^(N)Alloc_(res)(R _(i))+Σ_(j=1) ^(M)ask_(res)(FP_(j))+Ask_(res)(P)<=Total_forward_(res)

where Ask_(res)(P) is the number of resources “res” requested by ascheduled pending job in meta-scheduler 320. For example, if a jobrequests two 2 slots and one licA, if execution cluster 312 ₁ has tenavailable slots and no licA left, this execution cluster will be ignoredfor job forwarding scheduling.

After resource requirement evaluation, there will be multiple candidatemega-hosts 360 (and corresponding execution clusters 312) for the job(e.g., each passing job resource requirement evaluation and havingenough available forward resources to satisfy the reservation request).Meta-scheduler 320 employs an election algorithm to select the bestmega-host 360 (and execution cluster 312) for the job. The electionalgorithm enables the forwarding of jobs quicker and balances workloadamong back-end execution clusters 312. The election algorithm performstwo levels of sorting: execution cluster 312 sorting and mega-host 360sorting within each execution cluster 312. After sorting, the bestexecution cluster 312 will be selected to forward the job and themega-hosts 360 within this execution cluster 312 will be used for thereservation. The sorting algorithm considers the following two types ofresources, slots and numeric shared resources. The following exemplarysymbols/equations relate to slots in the sorting algorithm:

-   -   Total_(slot)(MH): total slots of candidate mega-host (MH).    -   Total_(slot)(C): total slots of all candidate mega-hosts in        execution cluster (C).        If Z is number of candidate mega-hosts in an execution cluster,        the value can be calculated as below:

Total_(slot)(C)=ΣΣ_(i=1) ^(Z)Total_(slot)(MH_(i))

-   -   Total_forward_(slot) (MH): total forward slots of candidate        mega-host MH.        Total_forward_(slot)(MH) will be calculated based on forwarding        resource Forward_ratio(MH) and Total_(slot)(MH):

Total_forward_(slot) (MH)=Forward_Ratio(MH)*Total_(slot)(MH)

-   -   Total_forward_(slot)(C): total forward slots of all candidate        mega-hosts in execution cluster C.

Total_forward_(slot)(C)=Σ_(i=1) ^(Z)Total_forward_(slot)(MH_(i))

-   -   Alloc_(slot) (R, MH): the amount of slots allocated to the        running job R on candidate mega-host MH.    -   Alloc_(slot) (R, C): the amount of slots allocated to the        running job R on all candidate mega-hosts in execution        cluster C. It can be calculated by:

Alloc_(slot)(R, C)=Σ_(i=1) ^(Z)Alloc_(slot)(R, MH_(i))

-   -   Ask_(slot)(FP, MH): the amount of slots requested by forwarded        pending jobs FP on candidate mega-host MH.    -   Ask_(slot) (FP, C): the amount of slots requested by forwarded        pending jobs FP on all candidate mega-hosts in execution        cluster C. It can be calculated by:

Ask_(slot) (FP, C)=Σ_(i=1) ^(Z)Ask_(slot) (FP, MH_(i))

For numeric shared resources, the following symbols/equations may bedefined: Total_(share)(C), Total_forward_(share)(C), Alloc_(share)(R,C), and Ask_(share)(FP, C) to represent the total number of numericshared resources “share” on execution cluster “C”, the total number ofnumeric shared forward resources “share” on execution cluster “C”, thenumber of allocated numeric shared resource “share” for the running jobRon execution cluster “C”, and the number of asked/requested numericshared resources “share” for a forwarding pending job FP on executioncluster “C”, respectively. The notations Ask_(slot) (P) andAsk_(share)(P) represent the number of requested slots and numericshared resource “share” for current scheduled pending jobs.

Available resource capacity represents logical available resources thatcan be allocated to a new running job. The available resource capacitycan be calculated based on total capacity of resources, total allocatedresources and the amount of requested resources for existing forwardingpending jobs as below:

Avail_cap_(res)=MAX(0, Total_(res)−Σ_(i=1) ^(N)Alloc_(res)(R_(i))−Σ_(i=1) ^(M)Ask_(res)(FP_(j)))Avail_cap_ratio_(res)=Avail_cap_(res)/Total_(res)

Available resource capacity ratio normalizes the available resourcecapacity based on total resources. In the election algorithm, theavailable resource capacity ratio is used to balance workload amongmega-hosts 360 (or execution clusters 312). Below are exemplarydefinitions/equations for available slots capacity and available slotscapacity ratio of candidate mega-host 360 and execution clusters 312:

-   -   Avail_cap_(slot)(MH): available slots capacity of candidate        mega-host MH. It can be calculated as below:

Avail_cap_(slot)(MH)=MAX(0, Total_(slot)(MH)−Σ_(i=1) ^(N)Alloc_(slot)(R_(i), MH)−Σ_(j=1) ^(M)Ask_(slot)(FP _(j)MH))

Avail_cap_ratio_(slot)(MH)=Avail_cap_(slot)(MH)/Total_(slot)(MH)

Avail_cap_(slot)(C): available slots capacity of all candidatemega-hosts in execution cluster C. The value can be calculated as below:

Avail_cap_(slot)(C)=Σ_(i=1) ^(Z)Avail_cap_(slot)(MH_(i))

Avail_cap_ratio_(slot)(C)=Avail_cap_(slot)(C)/Total_(slot)(C)

Similarly, Avail_cap_(share) (C) and Avail_cap_ratio_(share)(C)represent available numeric shared resource capacity and availablenumeric shared resource capacity ratio for resource “share”,respectively:

Avail_cap_(share)(C)=MAX(0, Total_(share)(C)−Σ_(i=1) ^(N)Alloc_(share)(R_(i), C)−Σ_(j=1) ^(M)Ask_(share)(FP _(j), C))Avail_cap_ratio_(share)(C)=Avail_cap_(share)(C)/Total_(share)(C)

“Pending Ratio” represents the relative pending queue length for eachmega-host 360 and execution cluster 312. The pending ratio can becalculated based on total resources, total allocated resources, totalamount of requested resources for forwarding pending jobs and totalforward resource as indicated below:

Pend_ratio_(res)=MAX(0,(Σ_(i=1) ^(N)Alloc_(res)(R _(i))+Σ_(j=1)^(M)Ask_(res)(FP _(j))−Total_(res)))/ Total_forward_(res)

For example, if execution cluster A has 100 slots, 150 forward slots, 90allocated slots and 30 forward pending slots, pending ratio will be(90+30−100)/150=0.13333. Again, pending ratio is used to balanceworkload among mega-hosts 360 and execution clusters 312. Slot pendingratio and numeric shared resource pending ratio will be defined asbelow:

-   -   Pend_ratio_(slot)(MH): slot pending ratio of candidate mega-host        MH. It can be calculated as below:

Pend_ratio_(slot)(MH)=MAX(0,(Σ_(i=1) ^(N)Alloc_(slot)(R _(i),MH)+Σ_(j=1) ^(M)Ask_(slot)(FP _(j),MH)−Total_(slot)(MH)))/Total_forward_(slot) (MH)

-   -   Pend_ratio_(slot)(C): slot pending ratio of all candidate        mega-hosts in execution cluster C. It can be calculated as        below:

Pend_ratio_(slot)(C)=(Σ_(i−1) ^(N)Alloc_(slot)(R _(i), C)+Σ_(j=1)^(M)Ask_(slot)(FP _(j), C)−Total_(slot)(C))/Total_forward_(slot)(C)

-   -   Pend_ratio_(share): numeric shared resource pending ratio. It        can be calculated as below:

Pend_ratio_(share)(C)=(Σ_(i=1) ^(N)Alloc_(share)(R _(i), C)+Σ_(j=1)^(M)Ask_(share)(FP _(j), C)−Total_(share)(C))/Total_forward_(share)(C)

When a job asks/requests for more than one type of resource, someresources may be more critical for execution cluster electioncalculation than others. For example, a certain software license can bevery expensive and relatively scarce compared with a computing slot.When a job requests for both this type of license and slot, if oneexecution cluster has an available license but no slot, while the secondexecution cluster does have the slot but no available license, the firstexecution cluster will be preferred instead of the second executioncluster. Resource weight factor (Weight_(res)) is used to represent theimportance of each type of resource. Different types of resources canhave different weight factors. The notations Weight_(slot) andWeight_(share) are used to represent corresponding slot weight andnumeric shared resource “share” weight values. The default values are 1,which are equally important.

As an example, consider that a particular job asks for or requests aslot and W types of numeric shared resources. The execution clusterelection algorithm will consider both slot and all requested sharedresources (share_1, share_2, . . . share_W). The notationall_share_status(P, C) represent the status of all available numericshared resources on execution cluster “C” for job P.

-   -   all_share_status(P, C): checking status of all available numeric        shared resources (share_1 . . . share_W) on execution cluster        “C” for job P.    -   1: all numeric shared resources, available resource capacity can        satisfy job requests.        -   For each share resource k in 1 . . . W

Avail_cap_(share) _(_) _(k)(C)>=Ask_(share) _(_) _(k)(P)

-   -   −1: all numeric shared resources, available resource capacity        cannot satisfy job requests        -   For each share resource k in 1 . . . W

Avail_cap_(share) _(_) _(k)(C)<ASk_(share) _(_) _(k)(P)

-   -   0: available resource capacity of some resources can satisfy job        requests        -   At least one resource k in 1 . . . W, Avail_cap_(share) _(_)            _(k)(C)>=Ask_(share) _(_) _(k)(P)        -   At least one resource m in 1 . . . W, Avail_cap_(share) _(_)            _(m)(C)<Ask_(share) _(_) _(m)(P)

The notations Total weighted avail cap ratio (P, C) represents weightedavailable resource capacity ratio, and Total_weighted_pend_ratio(P, C)represents weighted pending ratio as below:

Total_weighted_avail_cap_ratio(P,C)=Weight_(slot)*Avail_cap_ratio_(slot)(C)+Σ_(k=1) ^(W)Weight_(share)_(_) _(k)*Avail_cap_ratio_(share) _(_) _(k)(C)

Total weighted pend ratio(P, C) =Weight_(slot) *Pend_ratio_(slot)(C)+Σ_(k=1) ^(W)Weight_(share) _(_)k*Pend_ratio_(share) _(_) _(k)(C)

The election algorithm of meta-scheduler 320 compares candidateexecution clusters 312 one by one and picks the best one. The electionalgorithm handles three different scenarios when comparing two executionclusters 312, say C_(i) and C_(j):

-   -   1) Available slot capacity and all available numeric shared        resource capacity of one of the execution clusters that can        satisfy the job request:

If (Avail_cap_(slot) (C_(i)) >= Ask_(slot)(P) && all_share_status(P,C_(i)) > 0  && Avail_cap_(slot) (C_(j)) >= Ask_(slot)(P) &&all_share_status(P, C_(j)) > 0)    if (Total_weighted_avail_cap_ratio(P, C_(i)) >   Total_weighted_avail_cap_ratio(P, C_(j)) )      Pick C_(i)    else     Pick C_(j)    endif else if (Avail_cap_(slot) (C_(i)) >=Ask_(slot)(P) && all_share_status(P, C_(i)) > 0)    pick C_(i) else if(Avail_cap_(slot) (C_(j)) >= Ask_(slot)(P) && all_share_status(P,C_(j)) > 0)    pick C_(j) endif

-   -   2) Available slot capacity and all available numeric shared        resource capacity of both execution clusters cannot satisfy job        request:

If (Avail_cap_(slot) (C_(i)) < Ask_(slot)(P) && all_share_status(P,C_(i)) < 0  && Avail_cap_(slot) (C_(j)) < Ask_(slot)(P) &&all_share_status(P, C_(j)) < 0)    if ( Total_weighted_pend_ratio(C_(i))<    Total_weighted_pend_ratio(C_(j)) )      Pick C_(i)    else     Pick C_(j)    endif endif

-   -   3) For both clusters, either available slot capacity or some        available numeric shared resource capacity can satisfy the job        request. The election will consider        Total_weighted_avail_cap_ratio(P, C):

if ( Total_weighted_avail_cap_ratio(C_(i)) <Total_weighted_avail_cap_ratio(C_(j)) )   Pick C_(i) else   Pick C_(j)endif

After selecting the best candidate execution cluster 312, meta-scheduler320 will order the mega-hosts 360 within the selected candidateexecution cluster 312. The mega-host 360 ordering algorithm sorts andorders all candidate mega-hosts 360 by slot. IfAvail_cap_ratio_(slot)(MH) is greater than zero, place the mega-host 360with larger Avail_cap_ratio_(slot)(MH) value in the first place. If noneof the Avail_cap_ratio_(slot)(MH) is greater than zero, the job has towait in the queue. In this case, the mega-host 360 with smallerPend_ratio_(slot)(MH) value is placed in the first place. The followingis an exemplary ordering algorithm:

If (Avail_cap_ratio_(slot)(MH_(i)) > 0 ||Avail_cap_ratio_(slot)(MH_(j)) > 0 )   If(Avail_cap_ratio_(slot)(MH_(i)) > Avail_cap_ratio_(slot)(MH_(j)))    PutMH_(i) in the first place   Else If (Avail_cap_ratio_(slot)(MH_(i)) <Avail_cap_ratio_(slot)(MH_(j)))    Put MH_(j) in the first place   Else   If (Pend_ratio_(slot)(MH_(i)) < Pend_ratio_(slot)(MH_(j)))      PutMH_(i) in the first place    Else      Put MH_(j) in the first place   Endif   Endif Else   If (Pend_ratio_(slot)(MH_(i)) <Pend_ratio_(slot)(MH_(j)))    Put MH_(i) in the first place   Else   Put MH_(j) in the first place   Endif Endif

Meta-scheduler 320 may also utilize a share or fair-share policy for jobforwarding to execution clusters 312. A fair-share policy is configuredto divide the processing power of a cluster among multiple resourceconsumers (user groups, project groups, users) to provide fair access toresources. In some embodiments, the fair-share policy assigns a fixednumber of shares to each user or group. These shares represent afraction of the resources that are available in the execution clusters312. FIG. 5 is a diagram illustrating a hierarchy fair-share treeexample of share allocation according to the present disclosure. In FIG.5, there are ten tree nodes comprising a root node 510, three user groupnodes 512, 514 and 516, and six user nodes 520, 522, 524, 526, 528 and530. The most important users or groups are generally allocated agreater number of shares. FIG. 5 is a diagram illustrating an allocationof shares among different groups (e.g., project groups) and amongdifferent users of the respective groups. For example, user groups 512,514 and 516 are allocated 30, 20 and 50 shares, respectively. Each userwithin a particular user group may be allocated a fixed number orpercentage of the shares allocated to the respective user group. Forexample, in FIG. 5, user₁ is allocated 15 shares (e.g., 50% of theshares allocated to user group 512), user₂ is allocated 15 shares, user₃is allocated 8 shares (e.g., 40% of the shares allocated to user group514), user₄ is allocated 12 shares, user₅ is allocated 15 shares (e.g.,30% of the shares allocated to user group 516), and user₆ is allocated15 shares.

During scheduling, the fair-share scheduling algorithm of meta-scheduler320 calculates a dynamic user priority for users or groups, depending onhow the shares are assigned, the fair-share formula and the consumedresources. The priority is dynamic because it changes as soon as anyvariable in the formula changes. By default, a user's dynamic prioritygradually decreases after a job starts, and the dynamic priorityimmediately increases when the job finishes. The more resources thatworkloads consume, the less priority corresponding to the consumer/userwill be. Upon selecting the highest priority fair-share tree node,meta-scheduler 320 will select the first priority job under this treenode and schedule it. For a hierarchy tree structure, meta-scheduler 320may traverse the tree or hierarchy structure from top to bottom (the tophaving a greater number of shares), select the tree node with thehighest priority and continuing to deeper/lower levels. An exemplaryfair-share formula may include:

Dynamic_Priority=number_shares/weighted_resource_usage

-   -   Weighted resource usage can be calculated as follows:        -   Weighted_resource_usage=

(cpu_time*CPU_TIME_FACTOR

+run_time*RUN_TIME_FACTOR

+(1+job_slots)*RUN_JOB_FACTOR

+fairshare_adjustment*FAIRSHARE_ADJUSTMENT_FACTOR)

where number_shares: the number of shares assigned to the user;cpu_time: the cumulative CPU time used by the user; run_time: the totalrun time of running jobs; job_slots: the number of job slots reservedand in use from a user account, where Alloc_(slot)(R, C) is the totalresource allocation for running jobs at the cluster; andfairshare_adjustment: the adjustment calculated by customized externalfactors (e.g., job memory).

To support fair-share aware or share-based job forwarding bymeta-scheduler 320, for all factors required by the weighted resourceusage calculation, the back-end execution clusters 312 will updateworkload values to the front-end meta-scheduler 320. However, because aforwarded pending job may not be scheduled and run right away by aback-end execution cluster 312, resource usage alone may not fullysatisfy forwarding fair-share requests. As an example, consider if userAand userB have equal shares, userA submits all his workloads first, thenuserB's workloads. Meta-scheduler 320 selects userA's workload first.Since meta-scheduler 320 is efficient and can schedule many jobs withineach scheduling cycle, before userA jobs can run, userAweighted_resource_usage value will remain the same. In this case, alluserA jobs will be forwarded before any userB workload can beconsidered, which may cause unfair access to the back-end executionclusters 312. To handle this scenario, the fair-share formula considerscurrent consumed resources for all workloads of a fair-share resourceconsumer and requested resources by forwarding pending jobs. Anexemplary fair-share algorithm may be defined as:

Dynamic_Priority=number_shares/(weighted_resource_usage+forward_pending_slots*FORWARD_PENDING_FACTOR)

forward_pending_slots=Σ_(j=1) ^(M)Ask_(slot)(FP_(j))

where M: number of forwarded pending jobs belonging to each resourceconsumers.

With the foregoing process, as long as one consumer's workloads havebeen forwarded, the consumer's priority will be decreased. This willgive a chance for workloads from other consumers to be forwarded. FIG. 6is a diagram illustrating a job transition process using afair-share-forwarding policy according to the present disclosure. InFIG. 6, three job transition states are illustrated: PEND 610 (job ispending disposition to a particular execution cluster 312 (e.g., the jobis pending at submission cluster 310 awaiting forwarding bymeta-scheduler 320 to a particular execution cluster 312)); Forward PEND612 (job is in a forward pending queue of a particular execution cluster312); and RUN 614 (job is running in a particular execution cluster312). The forward_pending_slots value will be dynamically updated basedon job status transition as follows: PEND>Forward PEND: job isforwarded, forward_pending_slots will be increased by Ask_(slot)(FP);Forward PEND>PEND: job needs to be unforwarded (waiting too long, beingpulled back for re-scheduling etc.), forward_pending_slots will bedecreased by Ask_(slot)(FP); Forward PEND>RUN: job is being scheduled byexecution cluster scheduler 334/336 and runs, forward_pending_slots willbe decreased by Ask_(slot)(FP); RUN>Forward PEND: job is re-queued forre-scheduling, forward_pending_slots will be increased byAsk_(slot)(FP).

A generic job forwarding limit may also be used by meta-scheduler 320for forwarding jobs to back-end execution clusters 312. For example,many organizations deploy some form of resource limit policies toprevent a single user or group from excessively using the entirecomputing cluster. In a single cluster environment, the limit can be setto control a maximum amount of resources (e.g., slot, memory, swap,licenses, job and external resources, etc.) consumed by workloads frommultiple consumers (user, user groups, projects, queues, hosts, etc.).For example, user “John” from project “ABC” cannot use more than 1000cpus, etc. A similar concept can be applied by meta-scheduler 320 suchthat instead of controlling total consumed resources, a generic jobforwarding resource limit mechanism can be used to control totalreserved resources by forwarded jobs including both running and forwardpending workloads. The limits can be configured to enforce followingtypes of resources as an example:

-   -   Slots: total number of slots reserved by forwarded jobs.    -   Numeric shared resources: total number of numeric shared        resources reserved by forwarded jobs, for instance, software        licenses    -   Jobs: total number of forwarded jobs        The limits can also be applied to following types of consumers:    -   Users and user groups    -   Clusters    -   Queues    -   Projects        A consumer scope can be defined as:    -   Individual consumer: for instance, userA, execution clusterB    -   A list of consumers: for instance, usergroupA, usergroupB,        usergroupC    -   All consumers: for instance, all clusters    -   Per-consumer: for instance, per-user means every user

In some embodiments, consumers can be combined together. For example, alimit can be defined as 1000 cpus for jobs from project DesignModel onback-end execution cluster clusterA and clusterB. If a consumer is notspecified, it means “ALL” consumers. In the above example, it means allusers and all queues. During scheduling, meta-scheduler 320 checks thejob against all defined generic forwarding limits. If all of theforwarding resources have been reserved, no more jobs can be forwarded.In some embodiments, meta-scheduler 320 uses the following checkingcondition for each limit:

-   -   Forward_limit_(res): A generic limit defined for res (resource)        type; res can be numeric shared resource, slot or job        where N: number of running jobs belonging to limit consumers; M:        number of forwarded pending jobs belonging to limit consumers;        and Ask_(res)(P): the number of resources “res” requested by a        scheduling pending job belonging to limit consumers. Thus, if        the forwarding resources are less than or equal to        Forward_limit_(res), the job can be forwarded based on the        following exemplary formula:

Σ_(i=1) ^(N)Alloc_(res)(R_(i))|Σ_(j=1)^(M)Ask_(res)(FP_(j))+Ask_(res)(P)<=Forward_limit_(res)

Meta-scheduler 320 may also be configured to employ a mega-host 360cluster preference job forwarding policy. The job submission interfaceof submission cluster 310 may be configured to enable users to define alist of execution hosts 344 or execution host 344 groups as candidatesfor job processing. For example, in some embodiments, the job resourcerequirement data 322 may specify preference data 380 comprisinginformation associated with preferred execution hosts and/or levels ofpreference. To change the order of preference among candidates, theinterface may be configured to enable a plus (+) after the names ofexecution hosts 344 or execution host 344 groups that a user wouldprefer to use, optionally followed by a preference level. For preferencelevel, a positive integer may be specified with higher numbersindicating greater preferences for those execution hosts 344. It shouldbe understood that other methods may also be used to indicate hostpreferences and/or a level of preference. Meta-scheduler 320 may beconfigured with a job submission interface various cluster designationswith extension of cluster selection and preference as indicated below:

-   -   Host name[+pref_level]    -   Host group name[+pref_level]    -   Cluster name[+pref_level]

For example, a job submission preference string may indicate:“short_job_cluster+4 host_groupB+2 hostA” (which indicates that the jobpreference is to use cluster “short_job_cluster” first, then host group“host_groupB”, and the last preferred will be host “hostA”). In someembodiments, back-end execution cluster 344 local scheduler 334/336 willperform a final execution host 344 selection based on execution hostpreference. However, meta-scheduler 320 selects the mega-host 360 andcorresponding execution cluster 312 based on the specified executionhost and preference list. There may be two phases to resource matchingperformed by meta-scheduler 320: 1) mega-host 360/execution cluster 312mapping phase; and 2) mega-host 360/execution cluster 344 election.Meta-scheduler 320 receives host, host group and cluster membership andlocality information from back-end execution clusters 312.Meta-scheduler 320 has information of which execution host 344 belongsto which host group on which execution cluster 312. This informationwill be used during the host/cluster mapping phase. In the mappingphase, meta-scheduler 320 goes through specified host/cluster preferencerequest list information and translates the information into a mega-host360 preference request list and execution cluster 312 preference requestlist. Meta-scheduler 320 may use the following procedure:

-   -   For an individual execution host 344, find the corresponding        mega-host 360 this execution host 344 belongs to and place        mega-host 360 into the mega-host 360 preference request list and        keep preference level. Same thing applies to execution cluster        312 (place execution cluster 312 into the cluster preference        request list and keep preference level).    -   For execution host group, go through each execution 344 host in        the host group and use the similar approach as individual        execution host 344 and identify corresponding sets of mega-hosts        360 and place them into the mega-host 360 preference request        list and keep preference level. Same thing applies to cluster        312 (place execution clusters 312 of mega-hosts 360 into the        cluster preference request list and keep preference level).    -   For execution cluster 312, place all mega-hosts 360 within the        cluster 312 into the mega-host 360 preference request list and        keep preference level. Also place cluster 312 into the cluster        preference request list and keep preference level.    -   For mega-host 360 preference request list, if mega-host 360        appears multiple times, keep only one instance with maximal        preference level in the list. Order generated mega-host 360        preference request list based on preference level. The high        preference mega-host 360 will appear in the earlier place.    -   For the cluster 312 preference request list, if a cluster 312        appears multiple times, keep only one instance with maximal        preference level in the list. Order cluster preference request        list based on preference level. The high preference cluster will        appear in the earlier place.

At the end of the mapping phase, meta-scheduler 320 converts originalhost/cluster preference request lists into a mega-host 360 preferencerequest list and a cluster 312 preference request list. During themega-host 360/cluster 312 election phase, meta-scheduler 320 firstremoves all candidate mega-hosts 360 and clusters 312 that do not appearin the mega-host and cluster preference request lists. When comparingany two mega-hosts 360 or clusters 312, if mega-hosts 360 or clusters312 have a different preference level, meta-scheduler 320 may select themega-host 360 or cluster 312 with higher preference levels first basedon the mega-host and cluster preference request list. If two mega-hosts360 or clusters 312 have the equal preference level, meta-scheduler 320may use other/additional job forwarding policies to select a particularmega-host 360/cluster 312.

FIG. 7 is a flow diagram illustrating an embodiment of a method for jobdistribution within a grid environment according to the presentdisclosure. The method begins at block 702, where meta-scheduler 320determines resource attributes for execution hosts 344 of executionclusters 312. At block 704, meta-scheduler 320 selects one of theexecution clusters 312 and groups execution hosts 344 having the same orsimilar resource attributes to define mega-hosts 360 based on theresource attributes of execution hosts 344. At decisional block 706, adetermination is made whether there is another execution cluster 312within grid environment 302. If so, the method proceeds to block 704where meta-scheduler 320 continues to define mega-hosts 360 for eachexecution cluster 312. If at decisional block 706 is determined that nofurther execution clusters 312 require mega-host 360 definition, themethod proceeds to block 708, where submission cluster 310 receives jobs314 for processing.

At block 710, meta-scheduler 320 determines the resource requirementsfor the received jobs 314. At block 712, meta-scheduler 320 groups jobs314 based on the resource requirements of the jobs 314 (e.g., groupingjobs having the same or similar resource requirements). At block 714,meta-scheduler 320 sorts the groups of jobs 314 based on the user's 316submitting the jobs 314. At block 716, meta-scheduler 320 performsmega-host 360 matching to the sorted groups of jobs 314. At block 718,meta-scheduler 320 identifies candidate mega-hosts 360 for each of thegroups of jobs 314. At block 720, meta-scheduler 320 applies one or morejob forwarding policies to the groups of jobs (e.g., fair-shareforwarding, host preference forwarding, etc.). At block 722,meta-scheduler 320 selects a particular mega-host 360 for a particulargroup of jobs 314 and/or individual jobs 314. At block 724,meta-scheduler 320 distributes and/or otherwise forwards jobs toexecution clusters 312 based on the selection of correspondingmega-hosts 360.

FIG. 8 is a flow diagram illustrating another embodiment of a method forjob distribution within a grid environment according to the presentdisclosure. The method begins at block 802, where meta-scheduler 320determines the resource capacity of execution hosts 344 of executionclusters 312 (e.g., by defining mega-host 360 resource definitionscorresponding to the execution hosts 344 of each execution cluster 312).At block 804, submission cluster 310 receives jobs 314. At block 806,meta-scheduler 320 determines the resource requirements for the receivedjobs 314. At block 808, meta-scheduler 320 identifies candidateexecution clusters 312 (or mega-hosts 360) for running and/or processingthe submitted jobs 314. At block 810, 0meta-scheduler 320 determines anumber of running jobs 314 using resources of the identified candidateexecution clusters 312. At block 812, meta-scheduler 320 determines anumber of forwarded pending jobs 314 for the identified candidateexecution clusters 312. At block 814, meta-scheduler 320 determines anamount of resources allocated to running jobs 314 of the identifiedcandidate execution clusters 312. At block 816, meta-scheduler 320determines that amount of resources requested by forwarded pending jobs314 for the identified candidate execution clusters 312.

At block 818, meta-scheduler 320 dynamically determines a pending jobqueue length for the candidate execution cluster 312. At decisionalblock 820, meta-scheduler 320 determines whether the job resourcerequirements of the submitted jobs 314 include preference data 380. Ifnot, the method proceeds to block 828. If the submitted jobs includepreference data 380, the method proceeds from decisional block 820 toblock 822, where meta-scheduler 320 filters the candidate executionclusters 312 (or mega-hosts 360) based on the indicated preference data380. At decisional block 824, meta-scheduler 320 determines whether thepreference data 380 includes preference levels. If not, the methodproceeds to block 828. If the preference data 380 includes preferencelevels, the method proceeds from decisional block 824 to block 826,where meta-scheduler 320 sorts the preferred execution clusters 312 (ormega-hosts 360) based on the indicated preference levels. At block 828,meta-scheduler 320 identifies and/or otherwise selects an executioncluster 312 (or mega-host 360) for job processing. At block 830,meta-scheduler 320 forwards select jobs 314 to execution clusters 312according to the determined pending job queue length for thecorresponding execution host 344.

Thus, embodiments of the present disclosure provides a grid computingsystem that meets the performance requirements of large scale jobprocessing by performing a course granularity matching process tosubmitted jobs (e.g., using mega-host 360 definitions) to quicklyidentify execution clusters 312 having the resources to satisfy theresource requirements of the submitted jobs (e.g., instead of analyzingthe resources of each execution host). Embodiments of the presentdisclosure also provide scheduling efficiency by organizing jobs 314into groups to enable meta-scheduler 320 to schedule groups of jobs 314(e.g., instead of individual jobs). Embodiments of the presentdisclosure also provide a number of scheduling implementation forefficiently utilizing resources and scheduling jobs for back-endexecution cluster 312 processing. For example, by utilizing fair-sharepolicies regarding resource utilization (including a dynamic priorityfor users/user groups), a dynamic pending job queue length for executionclusters 312, a forwarding resource ratio for various resources of theexecution clusters 312 (which may be applied on a resource-specificbasis, applied to all resources of a particular type, applied toparticular execution clusters 312, applied to all execution clusters312, etc.) and/or a cluster 312 selection process that considershost-based resources and shared resources, including a weighting factorthat may be applied to the various resources, to efficiently select theoptimum execution cluster 312 for job processing.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosure.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the disclosure in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of the disclosure and the practical application, and toenable others of ordinary skill in the art to understand the disclosurefor various embodiments with various modifications as are suited to theparticular use contemplated.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

What is claimed is:
 1. A method for job distribution within a gridenvironment, comprising: receiving a job at a submission cluster fordistribution of the job to at least one of a plurality of executionclusters, each execution cluster comprising one or more execution hosts;determining resource attributes corresponding to each execution host ofthe execution clusters; grouping, for each execution cluster, executionhosts based on the resource attributes of the respective executionhosts; defining, for each grouping of execution hosts, a mega-host forthe respective execution cluster, the mega-host for a respectiveexecution cluster based on combining select resource attributes of therespective grouped execution hosts; determining resource requirementsfor the job; selecting an optimum execution cluster for receiving thejob based on a weighting factor applied to select resources of therespective execution clusters; identifying candidate mega-hosts withinthe optimum execution cluster for the job based on the resourceattributes of the respective mega-hosts and the resource requirements ofthe job; and selecting at least one of the candidate mega-hosts withinthe optimum execution cluster for allocating the job thereto forexecution of the job.
 2. The method of claim 1, further comprisinggrouping the execution hosts for a respective execution cluster based onresource slots and memory capacity attributes for the respectiveexecution hosts.
 3. The method of claim 1, further comprising sortingand ordering the candidate mega-hosts based on available resources ofthe respective mega-hosts.
 4. The method of claim 3, further comprisingsorting and ordering the candidate mega-hosts based on slotavailability.
 5. A system for job distribution within a gridenvironment, comprising: a submission cluster, having a processor, fordistributing a job to at least one of a plurality of execution clusters,wherein each execution cluster includes one or more execution hosts, andwherein the submission cluster comprises logic executable by a processorunit to: determine resource attributes corresponding to each executionhost of the execution clusters; group, for each execution cluster,execution hosts based on the resource attributes of the respectiveexecution hosts; define, for each grouping of execution hosts, amega-host for the respective execution cluster, the mega-host for arespective execution cluster based on combining select resourceattributes of the respective grouped execution hosts; determine resourcerequirements for the job; select an optimum execution cluster forreceiving the job based on a weighting factor applied to selectresources of the respective execution clusters; identify candidatemega-hosts within the optimum execution cluster for the job based on theresource attributes of the respective mega-hosts and the resourcerequirements of the job; and select at least one of the candidatemega-hosts within the optimum execution cluster for allocating the jobthereto for execution of the job.
 6. The system of claim 5, wherein thelogic is executable to group the execution hosts for a respectiveexecution cluster based on resource slots and memory capacity attributesfor the respective execution hosts.
 7. The system of claim 5, whereinthe logic is executable to sort and order the candidate mega-hosts basedon available resources of the respective mega-hosts.
 8. The system ofclaim 7, wherein the logic is executable to sort and order the candidatemega-hosts based on slot availability.
 9. A computer program product forjob distribution within a grid environment, the computer program productcomprising: a non-transitory computer readable medium having computerreadable program code embodied therewith, the computer readable programcode comprising computer readable program code configured to: receive ajob at a submission cluster for distribution of the job to at least oneof a plurality of execution clusters, each execution cluster comprisingone or more execution hosts; determine resource attributes correspondingto each execution host of the execution clusters; group, for eachexecution cluster, execution hosts based on the resource attributes ofthe respective execution hosts; define, for each grouping of executionhosts, a mega-host for the respective execution cluster, the mega-hostfor a respective execution cluster based on combining select resourceattributes of the respective grouped execution hosts; determine resourcerequirements for the job; select an optimum execution cluster forreceiving the job based on a weighting factor applied to selectresources of the respective execution clusters; identify candidatemega-hosts within the optimum execution cluster for the job based on theresource attributes of the respective mega-hosts and the resourcerequirements of the job; and select at least one of the candidatemega-hosts within the optimum execution cluster for allocating the jobthereto for execution of the job.
 10. The computer program product ofclaim 9, wherein the computer readable program code is configured togroup the execution hosts for a respective execution cluster based onresource slots and memory capacity attributes for the respectiveexecution hosts.
 11. The computer program product of claim 9, whereinthe computer readable program code is configured to sort and order thecandidate mega-hosts based on available resources of the respectivemega-hosts.
 12. The computer program product of claim 11, wherein thecomputer readable program code is configured to sort and order thecandidate mega-hosts based on slot availability.